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This study proposed an equation for Rainfall Threshold for Flood Warning (RTFW) for small urban watersheds based on computer simulations. First, a coupled 1D-2D dual-drainage model was developed for nine watersheds in Seoul, Korea. Next, the model simulation was repeated for a total of 540 combinations of the synthetic rainfall events and watershed imperviousness (9 watersheds x 4 NRCS Curve Number (CN) values x 15 rainfall events). Then, the results of the 101 simulations that caused the critical flooded depth (0.25m-0.35m) were used to develop the equation that relates the value of RTFW to the rainfall event temporal variability (represented as coefficient of variation or CV) and the watershed Curve Number. The results suggest that (1) RTFW exponentially decreases as the rainfall CV increases; (2) RTFW linearly decreases as the watershed CV increases; and that (3) RTFW is dominated by CV when the rainfall has low temporal variability (e.g., CV<0.2) while RTFW is dominated by CN when the rainfall has high temporal variability (e.g., CV>0.4). For validation, the proposed equation was applied for the flood warning system with two storm events occurred in 2010 and 2011 over 239 watersheds in Seoul. The system showed the the hit, false and missed alarm rates at 69% (48%), 31% (52%) and 6.7% (4.5%), respectively for the 2010 (2011) event.

In-situ gauge and weather radar are the two most widely applied measurement techniques to observe rainfall. The

The flood detection technique based on synthetic aperture radar (SAR) imagery was assessed. This technique was applied to a flood event in Busan in 2017. Sentinel-1 imagery was acquired, and then different multitemporal change detection methods were applied for SAR data analysis. Observed data were collected from newspapers, social media, and the Korean Fire Protection Association (KFPA). Specifically, flood and non-flood data were collected and used for analysis. As a result of receiver operating characteristic (ROC) curve analysis, the area under the curve (AUC) value was 0.67, which indicates a generally efficient technique, but it showed lower performance than the random model according to the threshold value. Four of 15 flood sites and a threshold value of 0.02 were selected to compare the flood area between the observed data and the flood maps derived from SAR analysis. As a result, all four flood maps included the observed flood areas, with some overestimation of flood area. A criterion for selecting the optimal threshold was suggested using ROC curve analysis, and the flood map results from this threshold were validated, but follow-up study is required to assess the accuracy numerically.